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AVL using Abaqus for thermal modeling of Li-ion batteries

04-Jun-2013 03:10 EDT

Abaqus FEA models of stabilized cell temperatures for three module designs with similar steady state temperatures. Compared with the baseline (left), the designs in the center and at right are smaller. However, the center design requires higher air flow.

Abaqus FEA models of stabilized cell temperatures for three module designs with similar steady state temperatures. Compared with the baseline (left), the designs in the center and at right are smaller. However, the center design requires higher air flow.

Good correlation of an indirect air-cool module under continuous discharge-charge condition.

There are many things that must be considered in the design of a battery system for use in vehicle electrification (EV, PHEV, or HEV), such as the power capacity of the battery system, cell selection, cell and module packaging constraints, pack architecture, volumetric and gravimetric energy densities of the pack, cost, structural durability of the battery system under various loadings, limiting ambient temperatures, etc.

“Thermal management of the battery packs is just one of many important considerations,” said Technical Specialist Kim Yeow, a member of the advanced simulation technology team at AVL’s technical center in Plymouth, MI. “From the battery performance and safety standpoint, we would like to maintain an optimal operating temperature range for the battery pack regardless of the ambient temperatures and operating conditions. We need to cool the batteries when they become too hot and to warm them up when their temperatures are too low.”

With 3-D FEA modeling, individual cell temperature patterns throughout the battery pack can be evaluated. The FEA model can identify the cell temperature distributions within the pack, which provides guidance in placement of temperature sensors in monitoring the operating temperatures of the battery pack. “The pack power capability is affected significantly by temperatures of the cells in the pack,” Yeow said. “If the temperature of a cell is too high, the power draw from the total pack must be reduced to prevent that cell from overheating. On the other hand, if the temperature is too low, it needs to be warmed up because low cell temperatures limit the power draw.” The maximum cell temperature and the maximum differential cell temperature are crucial factors to cell safety and durability.

For an internal R&D project to adapt a gasoline-powered car to an Electric Vehicle and Range Extender (EVARE) demonstration platform, the team at AVL was challenged to develop the battery pack configuration that would provide the most efficient battery cooling at the lowest possible cost. Key to this was a good understanding of what was happening inside the pack. “We needed a tool for analyzing the thermal behavior in a battery system, and the electro-thermal capabilities in Abaqus FEA [from SIMULIA, Dassault Systèmes] were a good fit to our need,” Yeow said. The software was already employed widely throughout AVL for conducting thermal-mechanical analyses on the engine component as well as non-engine work.

Because the tool was not specifically designed for battery simulation, the team at AVL wrote special subroutines and coupled them to Abaqus. “A good feature of the software is its ability to interface with user-written subroutines,” he said. “So we were able to capture the characteristics of the Li-ion cells using our own software and link them to the FEA model within Abaqus.”

The special subroutines developed for the EVARE project now enable AVL to evaluate the thermal behavior of assorted cell geometries and configurations, as well as the efficiency of various cooling methods. “One Li-ion battery is not like another, so for different cell types—whatever the geometry or chemistry—the cell geometric information and performance are inputs to our subroutines. This allows us to characterize the different cells,” Yeow said.

The team’s battery modeling typically starts with a quick 1-D simulation. “This gives us a sense on how the pack will work, on the basis of which we can dial in the cell selection and the cooling system requirement,” Yeow said. Once the full information on the pack’s cooling system is available, AVL performs a detailed 3-D assessment, beginning with electro-thermal analysis of the modules.

“Initially we assume good contact and hence good heat transfer paths between, say, the cooling plate and the cells,” Yeow said. “Abaqus is very good with contact, and all these components are touching each other. As the design matures, we’ll simulate the assembly condition to find out where there might be gaps and find ways to minimize those gaps, and re-evaluate how gaps in certain areas would impact the cooling of the battery cells.”

The complexity of the cell structures themselves adds more challenges to the task. Based on the cell capacity, a cell might have up to 50 pairs of thin anode-cathode layers, with the thickness of each layer on the order of 200 microns. “With 96 cells or more in a battery pack, it’s not practical to model the cell in such detail from engineering and production standpoint,” Yeow said. “So we approximate with one to three equivalent cell layers per cell and use the equivalent composite properties to characterize the behavior of the batteries at a macro level.

“Complicating things is the search for accurate material data,” he adds. “It’s always about solving the mystery. The cell manufacturers supply us with some ballpark numbers, but we often end up doing literature searches, or turning to other researchers, to get more accurate figures. Oftentimes we end up with a range of material values.

“It all trickles down to the model results,” he said. “The validity of that assumption will be measured against tests.”

Despite the many challenges, Yeow notes that they are seeing good correlation of their models to actual lab measurements, under either continuous discharge condition or continuous discharge-charge condition, with liquid cooling or with air cooling, with direct or indirect cooling systems. The correlated model was used to further improve and optimize the battery system design.

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